【摘要】 针对博物馆、展览馆等密集障碍物环境下腿脚不便群体的移动辅助需求，提出一种融合全局A~*算法与局部动态窗口法DWA的自主导航路径规划方法。首先通过引入启发式函数优化与路径平滑策略设计了改进A~*算法，利用该算法生成全局最优路径；然后针对动态环境特性采用自适应权重DWA算法进行实时避障与轨迹优化，在原始DWA算法的基础上加入全局路径评价函数和制动距离评价函数，显著提升复杂狭窄通道中的安全性与运动连续性。为验证算法有效性，在模拟展馆环境的实验室场景中构建测试平台。并引入轨迹平滑度、碰撞率及遍历节点数等指标对改进算法进行评估。实验结果表明，相较于传统A~*与原始DWA算法，本文方法在保证全局路径最优性的同时，不仅提高了动态障碍规避成功率，轨迹抖动幅度也有所降低。该方法为特殊群体在受限室内场景的安全移动提供了可靠技术支撑。更多还原

【Abstract】 Targeting the mobility assistance needs of people with walking difficulties in environments dense with obstacles like museums and exhibition halls, this study proposes an autonomous navigation path planning method. The method combines a global A~* algorithm with a local dynamic window approach DWA. First, an improved A~* algorithm designs an optimized heuristic function and a path smoothing strategy. This improved algorithm generates the global optimal path. Second, the study employs an adaptive weighting DWA algorithm to handle dynamic environments. This DWA variant adds a global path evaluation function and a braking distance evaluation function to the original DWA. These additions significantly enhance safety and motion continuity within complex, narrow passages. To verify effectiveness, the study constructs a test platform simulating a museum environment in a laboratory setting. Evaluation utilizes metrics including trajectory smoothness, collision rate and number of traversed nodes. Experimental results demonstrate that compared to traditional A~* and original DWA algorithms, this method achieves global path optimality. It also increases the dynamic obstacle avoidance success rate. Furthermore, the method reduces trajectory jitter amplitude. This approach provides reliable technical support for the safe movement of special groups in constrained indoor environments.更多还原